System and method to classify cardiopulmonary fatigue

ABSTRACT

This disclosure relates generally to classification of cardiopulmonary fatigue. The method and system provides a longitudinal monitoring platform to classify cardiopulmonary fatigue of a subject using a wearable device worn by the subject. The activities of the subject is continuous monitored by plurality of sensors embedded in a wearable device. The received sensor signals are processed in multiple stages to classify cardiopulmonary fatigue as healthy or unhealthy based on respiratory, heart rate and recovery duration parameters extracted from the received sensor data. Further using the classified cardiopulmonary fatigue level, the C2P also performs longitudinal analysis to detect potential cardiopulmonary disorders.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

The present application claims priority from Indian provisionalspecification no. 201721039348 filed on 4 Nov. 2017, the completedisclosure of which, in its entirety is herein incorporated byreferences.

TECHNICAL FIELD

The disclosure herein generally relates to field of cardiopulmonaryfatigue and, more particularly, to classification of cardiopulmonaryfatigue.

BACKGROUND

Cardiopulmonary disorders affect normal functioning of heart and lungs.The key contributors of the cardiopulmonary disorders are unhealthyeating habits and work environments. The cardiopulmonary diseases may beprevented by early detection and diagnosis of symptoms of thecardiopulmonary disorders. One of the commonly encountered symptoms ofcardiopulmonary disorder is fatigue.

Fatigue is a frequent complaint encountered with cardio diseases likeheart failure, valvular heart diseases, cardiomyopathies, coronaryartery disease. Fatigue is caused by abnormal stress, wherein abnormalstress is caused due to short spells of intensive activities in routinejobs like walking on stairs, brisk walking and so on. Hence while asubject is unobtrusively involved in activities, he may provide signsfor any possible detection of cardiopulmonary disorder at an earlystage.

Conventional clinical tools for detecting cardiopulmonary fatigue orstress level, require a subject to be monitored under supervision of anexpert in a lab. The existing clinical tools use various parameters of asubject such as Heart Rate (HR), Breathing Rate (BR) to monitor healthstatus of a subject, wherein the focus is on heart rate estimationrather than respiration monitoring. However, respiration monitoring isequally critical as power spectrum or scaled power ofbreathing/respiration state of a subject before and after activity issubstantially different and also results in more efficient estimation,when continuously monitored. Further for monitoring information recordedcontinuously over a period of time, frequent lab visits can be tedious,but can be reduced by introducing wearable devices such as smart watch,smart bands and so on for continuously collecting and monitoringrespiration and heart rate of a subject to enable effective detection ofcardiopulmonary fatigue that further detects potential symptoms ofcardiopulmonary disorders.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a method and system to classify cardiopulmonary fatigue isprovided. The proposed method and system is a Cardiopulmonary CarePlatform (C2P) that classifies cardiopulmonary fatigue level of asubject as healthy or unhealthy by analyzing sensor signal received froma subject using a wearable device worn by the subject. The receivedsensor signals is processed to classify cardiopulmonary fatigue ashealthy or unhealthy based on respiratory, heart rate and recoveryduration parameters extracted from the received sensor data. Furtherusing the classified cardiopulmonary fatigue level, the C2P alsoperforms longitudinal analysis to detect potential cardiopulmonarydisorders.

In another aspect, a method to classify cardiopulmonary fatigue isprovided. The method includes sensing a plurality of physiological datafrom the subject using a plurality of physiological sensors while thesubject is performing an activity. Further the method includes detectinga set of activity parameters using a plurality of activity detectors andextracting initial heart rate (HR), breathing rate (BR) and breathingsignal power (BP) from the sensed plurality of physiological data.Furthermore, the method includes obtaining metabolic equivalent (MET)values from a MET database based on the activity performed by thesubject during a pre-defined time interval, wherein the database storesMET values of multiple activities and estimating activity intensity (Al)of the subject for the pre-defined time interval based on the MET valuesand the subject data. Further the method includes estimating an expectedvalue of HR, BR and BP based on estimated Al, initial HR, BR, BP andtheir respective pre-determined normalizing constant and furtherestimating an expected recovery duration (RD) based on estimated Al andits respective normalizing constant. Further the method includesextracting actual HR, BR and BP from sensed plurality of physiologicaldata at the end of the activity performed by the subject and estimatingan actual recovery duration (RD) depending on the duration taken by thesubject for recovery. Further the method includes computing differencevalues between actual HR, BR, BP, RD and respective expected values ofHR, BR, BP and RD. Further the method includes estimating a deviationfactor based on the computed difference values and a pre-determinednormalizing constant. Finally the method includes classifying thesubject's fatigue as healthy or unhealthy fatigue based on thecomparison of the deviation factor with a pre-determined standard value.

In another aspect, a system to classify cardiopulmonary fatigue isprovided. The system comprises a memory storing instructions and acentralized database, one or more communication interfaces; and one ormore hardware processors coupled to the memory via the one or morecommunication interfaces, wherein the one or more hardware processorsare configured by instructions to: includes a pre-processing module aplurality of physiological sensors and a plurality of activity detectorsin wearable device for sensing a plurality of physiological data anddetection of activity performed from the subject. The system furtherincludes a physiological data extractor for extracting initial heartrate (HR), breathing rate (BR) and breathing signal power (BP) from thesensed plurality of physiological data. Furthermore the system includesa MET obtaining module for obtaining metabolic equivalent (MET) valuesfrom a MET database based on the activity performed by the subjectduring a pre-defined time interval, wherein the database stores METvalues of multiple activities. Further the system includes an activityintensity estimator for estimating activity intensity (Al) of thesubject for the pre-defined time interval based on the MET values andthe subject data and an expected value estimator for estimating anexpected value of HR, BR and BP based on estimated Al, initial HR, BR,BP and their respective pre-determined normalizing constant. Furthermorethe system includes an expected RD value estimator for estimating anexpected recovery duration (RD) based on estimated Al and its respectivenormalizing constant. Further the system includes an actual valueestimator for extracting actual HR, BR and BP from sensed plurality ofphysiological data at the end of the activity performed by the subject.Furthermore the system includes an actual RD value estimator forestimating an actual recovery duration (RD) depending on the durationtaken by the subject for recovery. Further the system includes adifference estimator for estimating difference values between actual HR,BR, BP, RD and respective expected values of HR, BR, BP and RD.Furthermore the system includes a deviation estimator for estimating adeviation factor based on the computed difference values and apre-demined normalizing constant. Furthermore the system includes aclassification module for classifying the subject's fatigue as healthyor unhealthy fatigue based on the comparison of the deviation factorwith a pre-determined standard value on the input/output interfaces.Finally the system comprises a longitudinal assessment module that isconfigured to performing longitudinal assessment of estimated Al, natureof fatigue caused, and physiological sensor data for several months todetect any underlying cardiopulmonary disorder.

In yet another aspect, a non-transitory computer readable medium toclassify cardiopulmonary fatigue is provided. The method includessensing a plurality of physiological data from the subject using aplurality of physiological sensors while the subject is performing anactivity. Further the method includes detecting a set of activityparameters using a plurality of activity detectors and extractinginitial heart rate (HR), breathing rate (BR) and breathing signal power(BP) from the sensed plurality of physiological data. Furthermore, themethod includes obtaining metabolic equivalent (MET) values from a METdatabase based on the activity performed by the subject during apre-defined time interval, wherein the database stores MET values ofmultiple activities and estimating activity intensity (Al) of thesubject for the pre-defined time interval based on the MET values andthe subject data. Further the method includes estimating an expectedvalue of HR, BR and BP based on estimated Al, initial HR, BR, BP andtheir respective pre-determined normalizing constant and furtherestimating an expected recovery duration (RD) based on estimated Al andits respective normalizing constant. Further the method includesextracting actual HR, BR and BP from sensed plurality of physiologicaldata at the end of the activity performed by the subject and estimatingan actual recovery duration (RD) depending on the duration taken by thesubject for recovery. Further the method includes computing differencevalues between actual HR, BR, BP, RD and respective expected values ofHR, BR, BP and RD. Further the method includes estimating a deviationfactor based on the computed difference values and a pre-determinednormalizing constant. Finally the method includes classifying thesubject's fatigue as healthy or unhealthy fatigue based on thecomparison of the deviation factor with a pre-determined standard value.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles:

FIG. 1 illustrates an exemplary block diagram of a system to classifycardiopulmonary fatigue in accordance with some embodiments of thepresent disclosure.

FIG. 2 is a functional block diagram of various modules stored inmodule(s) of a memory of the system of FIG. 1 in accordance with someembodiments of the present disclosure.

FIGS. 3A and 3B is an exemplary flow diagram illustrating a method toclassify cardiopulmonary fatigue using the system of FIG. 1 inaccordance with some embodiments of the present disclosure.

FIG. 4 illustrates before activity and after activity graphs of heartrate (HR), according to some embodiments of the present disclosure.

FIG. 5 illustrates before activity and after activity graphs ofbreathing signal power (BP), according to some embodiments of thepresent disclosure.

FIG. 6 illustrates before activity and after activity graphs ofbreathing rate (BR), according to some embodiments of the presentdisclosure.

FIGS. 7A and 7B illustrates before activity and after activity graphs oflow recovery duration (RD), according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. It is intendedthat the following detailed description be considered as exemplary only,with the true scope and spirit being indicated by the following claims.

Referring now to the drawings, and more particularly to FIG. throughFIG. 7B, where similar reference characters denote correspondingfeatures consistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1 illustrates an exemplary block diagram of a system 100 toclassify cardiopulmonary fatigue according to an embodiment of thepresent disclosure. In an embodiment, the system 100 includes memory102, one or more hardware processors 104, communication interfacedevice(s) or input/output (I/O) interface(s) 106, and one or more datastorage devices or memory 102 operatively coupled to the one or moreprocessors 104. The memory 102 comprises one or more modules 108 and thedatabase 110. The one or more processors 104 that are hardwareprocessors can be implemented as one or more microprocessors,microcomputers, microcontrollers, digital signal processors, centralprocessing units, state machines, logic circuitries, and/or any devicesthat manipulate signals based on operational instructions. Among othercapabilities, the processor(s) is configured to fetch and executecomputer-readable instructions stored in the memory. In an embodiment,the system 100 can be implemented in a variety of computing systems,such as laptop computers, notebooks, hand-held devices, workstations,mainframe computers, servers, a network cloud and the like.

The I/O interface device(s) 106 can include a variety of software andhardware interfaces, for example, a web interface, a graphical subjectinterface, and the like and can facilitate multiple communicationswithin a wide variety of networks N/W and protocol types, includingwired networks, for example, LAN, cable, etc., and wireless networks,such as WLAN, cellular, or satellite. In an embodiment, the I/Ointerface device(s) can include one or more ports for connecting anumber of devices to one another or to another server.

The memory 102 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes.

FIG. 2, with reference to FIG. 1, is a block diagram of various modules108 stored in the memory 102 of the system 100 of FIG. 1 in accordancewith an embodiment of the present disclosure. In an embodiment of thepresent disclosure, the system 100 comprises a wearable device (202)that further comprises of plurality of physiological sensors (204) and aplurality of activity detectors (206) in for sensing a plurality ofphysiological data and detecting a set of activity parametersrespectively from a subject using while the subject is performing anactivity. Further the system 100 includes a physiological data extractor(208) for extracting initial heart rate (HR), breathing rate (BR) andbreathing signal power (BP) from the sensed plurality of physiologicaldata. The system 100 further includes a MET obtaining module (210) forobtaining metabolic equivalent (MET) values. Further the system 100includes an activity intensity estimator (212) for estimating activityintensity (Al) of the subject. Further the system 100 includes anexpected value estimator (214) and an expected RD value estimator (216)for estimating an expected value of HR, BR and BP and for estimating anexpected recovery duration (RD) respectively. Further the system 100includes an actual value estimator (218) and an actual RD valueestimator (220) for extracting actual HR, BR and BP and for estimatingan actual recovery duration (RD) respectively. The system 100 furtherincludes a difference estimator (222) for estimating difference valuesbetween actual HR, BR, BP, RD and respective expected values of HR, BR,BP and RD. Further the system comprises of a deviation estimator (224)for estimating a deviation and finally a classification module (226) forclassifying the subject's fatigue as healthy or unhealthy fatigue basedon the comparison of the deviation factor with a pre-determined standardvalue on the input/output interfaces that are implemented as at leastone of a logically self-contained part of a software program, aself-contained hardware component, and/or, a self-contained hardwarecomponent with a logically self-contained part of a software programembedded into each of the hardware component that when executed performthe above method described herein.

According to an embodiment of the disclosure, the system 100 comprisesthe wearable device (202) that further comprises of plurality ofphysiological sensors (204) and a plurality of activity detectors (206)in for sensing a plurality of physiological data and detecting a set ofactivity parameters respectively from a subject using while the subjectis performing an activity.

In an embodiment, the plurality of physiological sensors (204) sensephysiological signals from the subject that includes photoplethysmogram(PPG) signals. The PPG signals are sensed at multiple time intervalsincluding rest time and activity time of the subject. Further a set ofactivity parameters are detected using a plurality of activity detectors(206) present on a non-intrusive wearable device (202) attached to thesubject. The activity detectors (206) further comprise of a plurality ofsensors that include inertial measurement unit (IMU), pressure sensor.The activity detectors (206) continuously monitors and detects anyactivity performed by the subject that includes brisk walking, movingup/down stairs, cycling.

According to an embodiment of the disclosure, the system 100 furthercomprises the physiological data extractor (208) that is configured toextract initial heart rate (HR), breathing rate (BR) and breathingsignal power (BP) from the sensed plurality of physiological data. Thesensor signals that include PPG signals are processed to extractphysiological features like complete breathing cycles that includebreathing rate (BR) and breathing signal power (BP), and heart rate (HR)using techniques known in art such as Fourier Transform based techniquesto obtain Power Spectral Density of signals filtered in respectivefrequency ranges of the BR, BP and HR.

According to an embodiment of the disclosure, the system 100 furthercomprises the MET obtaining module (210) that provides an exhaustivelist of a metabolic equivalent (MET) values for activity performed bythe subject. The MET obtaining module (210) obtains MET values from adatabase that stores MET values of multiple activities. The MET valuesare obtained for a pre-defined time interval, such as while performingactivity, wherein time (t_(b) denotes time while activity begins andtime (t_(e) denotes time while activity ends.

According to an embodiment of the disclosure, the system 100 furthercomprises the activity intensity estimator (212) that estimates activityintensity (Al) of the subject for the pre-defined time interval based onthe MET values and the subject data. Activity Intensity (Al) of thesubject is estimated based on MET and user profile data, that includessubject's body weight (W), a pre-defined time interval (T), thesubject's level of physical activity in daily life (LPA), gender (G),age (A) and pre-determined normalization constant ‘N’, which isexpressed as follows;

I=f _(i)(MET,W,T,LPA,G,A,N)  (1)

Where

-   -   W is subject's body weight    -   T is pre-defined time interval    -   LPA is level of physical activity in daily life of the subject    -   G is subject's gender    -   A is subject's age and    -   N is pre-determined normalization constant

According to an embodiment of the disclosure, the system 100 furthercomprises the expected value estimator (214) that is configured toestimate an expected value of HR, BR and BP. The expected value of HR,BR and BP are estimated based on Al, initial HR, BR, BP and theirrespective pre-determined normalizing constant, which is expressed asshown below;

HR_(f) =f _(hr)(HR_(i) ,I,N _(HR))  (2)

BR_(f) =f _(br)(BR_(i) ,I,N _(RR))  (3)

BP_(f) =f _(bp)(BP_(i) ,I,N _(BP))  (4)

Where

-   -   HR_(f) is expected HR    -   BR_(f) is expected BR    -   BP_(f) is expected BP    -   f and I denote after and before time interval respectively    -   N_(HR), N_(BR) and N_(BP) is pre-determined normalizing constant

According to an embodiment of the disclosure, the system 100 furthercomprises the expected RD value estimator (216) that is configured toestimate an expected recovery duration (RD). The recovery duration (RD)is the time taken by the subject to reach a baseline heart rate afterthe end of the activity, wherein the baseline heart rate is basal heartrate of the subject at rest. The expected recovery duration (RD) isestimated depending on the duration taken by the subject for recoveryafter performing an activity, which is expressed as shown below;

RD_(f) =f _(rd)(I,N _(RD))  (5)

Where

-   -   RD_(f) is expected RD    -   f and I denote after and before time interval respectively    -   N_(RD) is pre-determined normalizing constant

According to an embodiment of the disclosure, the system 100 furthercomprises the actual value estimator (218) that is configured toestimate an actual HR, BR and BP from sensed plurality of physiologicaldata at the end of the activity performed by the subject. The actual HR,BR and BP are denoted as HR_(a), BR_(a) and BP_(a).

According to an embodiment of the disclosure, the system 100 furthercomprises the actual RD value estimator (220) that is configured toestimate an actual recovery duration (RD) at the end of the activityperformed by the subject. The actual RD is denoted as RD_(a).

According to an embodiment of the disclosure, the system 100 furthercomprises the difference estimator (222) that is configured to estimatedifference values between actual HR, BR, BP, RD and respective expectedvalues of HR, BR, BP and RD, which is expressed as shown below;

HR_(d)=HR_(a)−HR_(f)  (6)

BR_(d)=BR_(a)−BR_(f)  (7)

BP_(d)=BP_(a)−BP_(f)  (8)

RD_(d)=RD_(a)−RD_(f)  (9)

According to an embodiment of the disclosure, the system 100 furthercomprises the deviation estimator (224) that is configured to estimate adeviation factor based on the computed difference values and apre-determined normalizing constant.

DF=f _(d)(HR_(d),BR_(d),BP_(d),RD_(d) ,N _(d))  (10)

Where

f_(d) is a norm function.

DF can also be alternatively expressed as shown below;

DF=√{square root over ((N ₁ XHR_(d))²+(N ₂ XBR_(d))²+(N ₃ XBP_(d))²+(N ₄XRD_(d))²)}  (11)

According to an embodiment of the disclosure, the system 100 furthercomprises the classification module (226) that is configured to classifya subject's cardiopulmonary fatigue as healthy or unhealthycardiopulmonary fatigue. The classification of cardiopulmonary fatigueis based on the comparison of the deviation factor with a pre-determinedstandard value. Further the classified cardiopulmonary fatigue isdisplayed on input/output (I/O) interface (106).

According to an embodiment of the disclosure, the system 100 furthercomprises the longitudinal assessment module (228) that is configured toperforming longitudinal assessment of estimated Al, nature of fatiguecaused, and physiological sensor data for several months to detect anyunderlying cardiopulmonary disorder. The longitudinal assessment modulecan be used by any medical expert to track health of any subject over aperiod of time by monitoring the patterns of detected cardiopulmonaryfatigue caused to the subject owing to different activities performed inroutine life, which may detect any potential cardiopulmonary disorder.

FIGS. 3A and 3B, with reference to FIGS. 1-2, is an exemplary flowdiagram illustrating a method to classify cardiopulmonary fatigue usingthe system 100 of FIG. 1 according to an embodiment of the presentdisclosure. In an embodiment, the system 100 comprises one or more datastorage devices or the memory 102 operatively coupled to the one or morehardware processors 104 and is configured to store instructions forexecution of steps of the method by the one or more processors 104. Thesteps of the method of the present disclosure will now be explained withreference to the components of the system 100 and the modules 302-324 asdepicted in FIGS. 1-2, and the flow diagram as depicted in FIGS. 3A and3B.

At step 302, a plurality of physiological data are sensed from a subjectusing the plurality of physiological sensors (204) while the subject isperforming an activity. The plurality of physiological sensors arepresent on the non-intrusive wearable device (202) attached to thesubject. The plurality of physiological sensors (204) sensephysiological signals from the subject that includes photoplethysmogram(PPG) signals. The PPG signals are sensed at multiple time intervalsincluding rest time and activity time of the subject.

In the next step at 304, a set of activity parameters are detected usingthe plurality of activity detectors (206). The plurality of activitydetectors (206) are present on a non-intrusive wearable device (202)attached to the subject. The activity detectors (206) further compriseof a plurality of sensors that include inertial measurement unit (IMU),pressure sensor. The activity detectors (206) continuously monitors anddetects any activity performed by the subject that includes briskwalking, moving up/down stairs, cycling.

In the next step at 306, initial heart rate (HR), breathing rate (BR)and breathing signal power (BP) are extracted from the sensed pluralityof physiological data by the physiological data extractor (208). Thesensor signals that include PPG signals are processed to extractphysiological features like complete breathing cycles that includebreathing rate (BR) and breathing signal power (BP), and heart rate (HR)using techniques known in art such as Fourier Transform based techniquesto obtain Power Spectral Density of signals filtered in respectivefrequency ranges of the BR, BP and HR.

In the next step at 308, metabolic equivalent (MET) values for activityperformed by the subject is obtained from the MET obtaining module(210). The MET obtaining module (210) obtains MET values from a databasethat stores MET values of multiple activities. The MET values areobtained for a pre-defined time interval, such as while performingactivity, wherein time (t_(b)) denotes time while activity begins andtime (t_(e)) denotes time while activity ends.

In the next step at 310, activity intensity (Al) of the subject isestimated for the pre-defined time interval based on the MET values andthe subject data in the activity intensity estimator (212). ActivityIntensity (Al) of the subject is estimated based on MET and user profiledata, that includes subject's body weight (W), the pre-defined timeinterval (T), subject's level of physical activity in daily life (LPA),gender (G), age (A) and pre-determined normalization constant (‘N’).

In the next step at 312, an expected value of HR, BR and BP is estimatedin the expected value estimator (214). The expected value of HR, BR andBP are estimated based on Al, initial HR, BR, BP and their respectivepre-determined normalizing constant.

In the next step at 314, an expected recovery duration (RD) is estimatedthe expected RD value estimator (216). The recovery duration (RD) is thetime taken by the subject to reach a baseline heart rate after the endof the activity, wherein the baseline heart rate is basal heart rate ofthe subject at rest. The expected recovery duration (RD) is estimateddepending on the duration taken by the subject for recovery afterperforming an activity.

In the next step at 316, actual HR, BR and BP are extracting from sensedplurality of physiological data at the end of the activity performed bythe subject the actual value estimator (218).

In the next step at 318, an actual recovery duration (RD) is estimateddepending on the duration taken by the subject for recovery in theactual RD value estimator (220).

In the next step at 320, difference values between actual HR, BR, BP, RDand respective expected values of HR, BR, BP and RD is computed in thedifference estimator (222).

In the next step at 320, a deviation factor is estimated based on thecomputed difference values and a pre-determined normalizing constant inthe deviation estimator (224).

In the next step at 322, subject's cardiopulmonary fatigue is classifiedas healthy or unhealthy cardiopulmonary fatigue based on the comparisonof the deviation factor with a pre-determined standard value in theclassification module (226). Further the classified cardiopulmonaryfatigue is displayed on input/output (I/O) interface (106).

FIG. 4 illustrates before activity and after activity graphs of heartrate (HR), according to some embodiments of the present disclosure. Inan embodiment, the graph illustrates the heart rate (HR) before andafter activity, which is plotted on Y-Axis with respect a window of timeinterval on X-Axis.

FIG. 5 illustrates before activity and after activity graphs ofbreathing signal power (BP), according to some embodiments of thepresent disclosure. In an embodiment, the graph illustrates thebreathing signal power (BP) before and after activity, which is plottedon Y-Axis with respect a window of time interval on X-Axis.

FIG. 6 illustrates before activity and after activity graphs ofbreathing rate (BR), according to some embodiments of the presentdisclosure. In an embodiment, the graph illustrates the breathing rate(BR) before and after activity, which is plotted on Y-Axis with respecta window of time interval on X-Axis.

FIGS. 7A and 7B illustrates before activity and after activity graphs ofrecovery duration (RD), according to some embodiments of the presentdisclosure. In an embodiment, the graph illustrates the recoveryduration (RD) recovery duration (RD), before and after activity, whichis plotted on Y-Axis with respect a window of time interval on X-Axis.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

Hence a method and a system to classify cardiopulmonary fatigue isprovided. The proposed method and system is a Cardiopulmonary CarePlatform (C2P) that classifies cardiopulmonary fatigue level of asubject as healthy or unhealthy by analyzing sensor signal received froma subject using a wearable device worn by the subject. The receivedsensor signals is processed to classify cardiopulmonary fatigue ashealthy or unhealthy based on respiratory, heart rate and recoveryduration parameters extracted from the received sensor data. Furtherusing the classified cardiopulmonary fatigue level, the C2P alsoperforms longitudinal analysis to detect potential cardiopulmonarydisorders.

It is to be understood that the scope of the protection is extended tosuch a program and in addition to a computer-readable means having amessage therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software modules located therein. Thus, themeans can include both hardware means and software means. The methodembodiments described herein could be implemented in hardware andsoftware. The device may also include software means. Alternatively, theembodiments may be implemented on different hardware devices, e.g. usinga plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various modules described herein may be implemented in other modulesor combinations of other modules. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan comprise, store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments. Also, the words “comprising,”“having,” “containing,” and “including,” and other similar forms areintended to be equivalent in meaning and be open ended in that an itemor items following any one of these words is not meant to be anexhaustive listing of such item or items, or meant to be limited to onlythe listed item or items. It must also be noted that as used herein andin the appended claims, the singular forms “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A processor-implemented method to classifycardiopulmonary fatigue of a subject, the method comprising: sensing aplurality of physiological data from the subject using a plurality ofphysiological sensors while the subject is performing an activity (302);detecting a set of activity parameters using a plurality of activitydetectors (304); extracting initial heart rate (HR), breathing rate (BR)and breathing signal power (BP) from the sensed plurality ofphysiological data (306); obtaining metabolic equivalent (MET) valuesfrom a MET database based on the activity performed by the subjectduring a pre-defined time interval, wherein the database stores METvalues of multiple activities (308); estimating activity intensity (Al)of the subject for the pre-defined time interval based on the MET valuesand the subject data (310); estimating an expected value of HR, BR andBP based on estimated Al, initial HR, BR, BP and their respectivepre-determined normalizing constant (312); estimating an expectedrecovery duration (RD) based on estimated Al and its respectivenormalizing constant (314); extracting actual HR, BR and BP from sensedplurality of physiological data at the end of the activity performed bythe subject (316); estimating an actual recovery duration (RD) dependingon the duration taken by the subject for recovery (318); computingdifference values between actual HR, BR, BP, RD and respective expectedvalues of HR, BR, BP and RD (320); estimating a deviation factor basedon the computed difference values and a pre-determined normalizingconstant (322); classifying the subject's fatigue as healthy orunhealthy fatigue based on the comparison of the deviation factor with apre-determined standard value (324).
 2. The method of claim 1, whereinthe plurality of physiological sensors and activity detectors arepresent on a non-intrusive wearable device attached to the subject. 3.The method of claim 1 wherein the plurality of physiological sensor dataincludes photoplethysmogram (PPG) sensed at multiple time intervalsincluding rest time and activity time of the subject.
 4. The method ofclaim 1 includes estimating Activity Intensity (Al) of the subject basedon MET and user profile data, that includes subject's body weight (W), apre-defined time interval (T), subject's level of physical activity indaily life (LPA), gender (G), age (A) and pre-determined normalizationconstant (‘N’).
 5. The method of claim 1, where in the recovery duration(RD) is the time taken by the subject to reach a baseline heart rateafter the end of the activity, wherein the baseline heart rate is basalheart rate of the subject at rest.
 6. The method of claim 1 furthercomprising the step of displaying subject's activity as healthy orunhealthy fatigue on an input/output (I/O) interface.
 7. The method ofclaim 1 further comprising the step of performing longitudinalassessment of estimated Al, nature of fatigue caused, and physiologicalsensor data over several months to detect any underlying cardiopulmonarydisorder.
 8. A system to classify cardiopulmonary fatigue of a subject,comprising: a memory (102) storing instructions and one or more modules(108); a database (110); one or more communication or input/outputinterfaces (106); and one or more hardware processors (104) coupled tothe memory (102) via the one or more communication interfaces (106),wherein the one or more hardware processors (104) are configured by theinstructions to execute the one or more modules (108) comprising: aplurality of physiological sensors (204) in wearable device (202) forsensing a plurality of physiological data from the subject using whilethe subject is performing an activity; a plurality of activity detectors(206) in wearable device (202) detecting a set of activity parameters; aphysiological data extractor (208) for extracting initial heart rate(HR), breathing rate (BR) and breathing signal power (BP) information(BP) from the sensed plurality of physiological data; a MET obtainingmodule (210) for obtaining metabolic equivalent (MET) values from a METdatabase based on the activity performed by the subject during apre-defined time interval, wherein the database stores MET values ofmultiple activities; an activity intensity estimator (212) forestimating activity intensity (Al) of the subject for the pre-definedtime interval based on the MET values and the subject data; an expectedvalue estimator (214) for estimating an expected value of HR, BR and BPbased on estimated Al, initial HR, BR, BP and their respectivepre-determined normalizing constant; an expected RD value estimator(216) for estimating an expected recovery duration (RD) based onestimated Al and its respective normalizing constant; an actual valueestimator (218) for extracting actual HR, BR and BP from sensedplurality of physiological data at the end of the activity performed bythe subject; an actual RD value estimator (220) for estimating an actualrecovery duration (RD) depending on the duration taken by the subjectfor recovery; a difference estimator (222) for estimating differencevalues between actual HR, BR, BP, RD and respective expected values ofHR, BR, BP and RD; a deviation estimator (224) for estimating adeviation factor based on the computed difference values and apre-determined normalizing constant; and a classification module (226)for classifying the subject's fatigue as healthy or unhealthy fatiguebased on the comparison of the deviation factor with a pre-determinedstandard value on the input/output interfaces (106).
 9. The system ofclaim 8 further comprising longitudinal assessment module (228) forperforming longitudinal assessment of estimated Al, nature of fatiguecaused, and physiological sensor data continuously over several monthsto detect any underlying cardiopulmonary disorder.
 10. Thenon-transitory computer-readable medium having embodied thereon acomputer program to classify cardiopulmonary fatigue of a subject, themethod comprising: sensing a plurality of physiological data from thesubject using a plurality of physiological sensors while the subject isperforming an activity; detecting a set of activity parameters using aplurality of activity detectors; extracting initial heart rate (HR),breathing rate (BR) and breathing signal power (BP) from the sensedplurality of physiological data; obtaining metabolic equivalent (MET)values from a MET database based on the activity performed by thesubject during a pre-defined time interval, wherein the database storesMET values of multiple activities; estimating activity intensity (Al) ofthe subject for the pre-defined time interval based on the MET valuesand the subject data; estimating an expected value of HR, BR and BPbased on estimated Al, initial HR, BR, BP and their respectivepre-determined normalizing constant; estimating an expected recoveryduration (RD) based on estimated Al and its respective normalizingconstant; extracting actual HR, BR and BP from sensed plurality ofphysiological data at the end of the activity performed by the subject;estimating an actual recovery duration (RD) depending on the durationtaken by the subject for recovery; computing difference values betweenactual HR, BR, BP, RD and respective expected values of HR, BR, BP andRD (320); estimating a deviation factor based on the computed differencevalues and a pre-determined normalizing constant; classifying thesubject's fatigue as healthy or unhealthy fatigue based on thecomparison of the deviation factor with a pre-determined standard value.